Error Analysis of Parameter Prediction via Gaussian Process Regression and Its Application to Weighted Jacobi Iteration

Fuente: arXiv
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Autores principales: Sun, Tiantian, Zhang, Juan
Formato: Preprint
Publicado: 2026
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author Sun, Tiantian
Zhang, Juan
author_facet Sun, Tiantian
Zhang, Juan
contents In this paper, we introduce a novel theoretical framework for Gaussian process regression error analysis, leveraging a function-space decomposition. Based on this framework, we develop a weighted Jacobi iterative method that utilizes Gaussian process regression for parameter prediction and provide a corresponding convergence analysis. Moreover, the convergence conditions are designed to be compatible with other error bounds, enabling a more general analysis. Experimental results show that the parameters predicted based on Gaussian process regression significantly accelerate the convergence speed of Jacobi iterations.
format Preprint
id arxiv_https___arxiv_org_abs_2602_22679
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Error Analysis of Parameter Prediction via Gaussian Process Regression and Its Application to Weighted Jacobi Iteration
Sun, Tiantian
Zhang, Juan
Numerical Analysis
In this paper, we introduce a novel theoretical framework for Gaussian process regression error analysis, leveraging a function-space decomposition. Based on this framework, we develop a weighted Jacobi iterative method that utilizes Gaussian process regression for parameter prediction and provide a corresponding convergence analysis. Moreover, the convergence conditions are designed to be compatible with other error bounds, enabling a more general analysis. Experimental results show that the parameters predicted based on Gaussian process regression significantly accelerate the convergence speed of Jacobi iterations.
title Error Analysis of Parameter Prediction via Gaussian Process Regression and Its Application to Weighted Jacobi Iteration
topic Numerical Analysis
url https://arxiv.org/abs/2602.22679